One of many normalizing methods is to obtain histograms of an extracted character vertically and horizontally. Characters are magnified or contracted to be normalized from the obtained histograms.
However, this normalizing method has the disadvantage that it consumes much time to obtain the histograms, because the CPU performs processing for magnification or contraction in one pixel or one-dot units.
Feature data of stored characters are read from a dictionary in character units and are then compared with the feature data of an input character to obtain a recognition. This has the disadvantage that the comparison takes much time.
Present character recognition devices need to be operated faster and to have a better recognition rate. However to improve the recognition rate, a greater volume of data must be handled. This causes the problem that the operation speed is slowed. Conversely, for faster operation, the data volume must be reduced. This causes the problem that the recognition accuracy is reduced.
That is, higher accuracy and higher speed are imcompatible with optical character recognition. In particular, since the pre-treatment mentioned earlier is required to handle image data, it takes a long time to process them. This becomes the bottleneck in the entire processings.
This invention aims at realizing a data processing system which enables a simple circuit to magnify or contract dot or pixel rows in parallel, expedites histogram calculation by using a two-port DRAM, enables a character recognition device to speed up pre-treatment of high-speed character recognition for a large volume of data, rushes pattern recognition by parallelly processing histogram information for normalizing extracted character data in a pipeline, and has a systolic array speed up pattern recognition by parallelly processing inputted data in a pipeline.